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@jinhwanlazy
Last active Feb 15, 2017
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typeracer data plotter
"""
Plots race TypeRacer history.
Make sure you have permission to read the file
usage:
$ python3 race_plot.py race_data.csv [day, week, month or year]
"""
import sys
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import cm
from datetime import datetime, timezone, timedelta
plt.style.use('ggplot')
ticks_format = {
'day': '%b, %-d',
'week': '%b, %-d',
'month': '%b',
'year': '%Y'
}
def accuracy_to_color(acc_df):
return ((acc_df-0.9).clip(0, 0.1) * 1000).astype(int)
def group_date(gr):
def str_to_datetime(s):
date_format = "%Y-%m-%d %H:%M:%S"
return datetime.strptime(s, date_format)
def utc_to_local(utc_dt):
return utc_dt.replace(tzinfo=timezone.utc).astimezone(tz=None)
group_method = {
'day': datetime.date,
'week': lambda dt: dt.date() + timedelta(days=6-(dt.weekday() % 7)),
'month': lambda dt: datetime(dt.year, dt.month, 1),
'year': lambda dt: datetime(dt.year, 1, 1)
}
return lambda s: group_method[gr](utc_to_local(str_to_datetime(s)))
def last_race_of_day(df, date):
return df.loc[df['date'] == date]['Race #'].max()
def step_plot(ax, df, col, color, label):
def average_of_day(df, col):
ret = {}
for date in set(df['date']):
data = df.loc[df['date'] == date]
ret[date] = data[col].mean()
return ret
avg = average_of_day(df, col)
avg = sorted(avg.items())
v = [(0, avg[0][1])]
if len(avg) > 1:
for (d1, v1), (d2, v2) in zip(avg, avg[1:]):
v.append(((last_race_of_day(df, d1)+0.5, v1)))
v.append(((last_race_of_day(df, d1)+0.5, v2)))
v.append(((last_race_of_day(df, d2)+0.5, v2)))
else:
v.append(((last_race_of_day(df, avg[0][0])+0.5, avg[0][1])))
return ax.plot(*zip(*v), linewidth=2, c=color, label=label)
if __name__ == "__main__":
data = pd.read_csv(sys.argv[1], sep=',')
gr = sys.argv[2] if (
len(sys.argv) > 2 and sys.argv[2] in ticks_format) else 'day'
data['date'] = data['Date/Time (UTC)'].map(group_date(gr))
fig, ax = plt.subplots()
ticks = []
for date in set(data['date']):
vert_line = last_race_of_day(data, date) + 0.5
ax.axvline(x=vert_line, linestyle='dashed', color='black', linewidth=1)
ticks.append((vert_line, date.strftime(ticks_format[gr])))
plt.xticks(*zip(*ticks), rotation='60')
for tick in ax.xaxis.get_majorticklabels():
tick.set_horizontalalignment("right")
cax = ax.scatter(data['Race #'], data['WPM'],
c=accuracy_to_color(data['Accuracy']),
cmap=cm.Oranges, label=None)
ax2 = ax.twinx()
ax2.grid(False)
line_acc = step_plot(ax2, data, 'Accuracy', 'blue', 'Accuracy')
line_wpm = step_plot(ax, data, 'WPM', 'red', 'WPM')
ax.set_ylabel('WPM')
ax2.set_ylabel('Accuracy')
lns = line_wpm + line_acc
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=2)
plt.title('Total %d games' % len(data))
plt.show()
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